Parametric images are commonly used for graphically representing the result of quantitative analysis processes in diagnostic applications. Particularly, this technique may be used for the assessment of blood perfusion in contrast-enhanced ultrasound imaging applications. For this purpose, an ultrasound contrast agent (UCA)—for example, consisting of a suspension of phospholipid-stabilized gas-filled microbubbles—is administered to a patient. The contrast agent acts as an efficient ultrasound reflector, and can be easily detected by applying ultrasound waves and measuring the echo signals that are returned in response thereto. Since the contrast agent flows at the same velocity as red-blood cells in the patient, its detection and tracking provides information about blood perfusion in a body-part under analysis. Particularly, the echo signal that is recorded over time for each location of the body-part is associated with a model function of time; the model function is used to calculate the value of any desired perfusion parameter (for example, a wash-in rate), which characterizes the location of the body-part. A parametric image is then generated by assigning, to each pixel representing a location of the body-part, the corresponding perfusion parameter value. The display of this parametric image shows the spatial distribution of the perfusion parameter values throughout the body-part, especially when the parametric image (being color-coded) is overlaid on a morphological image representing it; this facilitates the identification and characterization of possible locations of the body-part that are abnormally perfused (for example, because of a pathological condition).
However, the parametric images do not reflect a dynamic behavior of each location of the body-part during the analysis process; particularly, they are not capable of representing the kinetics of the corresponding perfusion. Therefore, the parametric images provide quite poor results in specific diagnostic applications (which are mainly based on differences in the perfusion kinetics); a typical example is the characterization of Focal Liver Lesions (FLLs), which exhibit a Dynamic Vascular Pattern (DVP) that substantially differs from the one of healthy parenchyma.
A specific technique based on the use of a parametric image for characterizing lesions in the liver is described in WO-A2-06/090309 (the entire disclosure of which is herein incorporated by reference). In this case, the locations that exhibit an early wash-in, indicative of HepatoCellular Carcinoma (HCC) lesions, are highlighted in the parametric image. These locations are determined by means of a classifier; particularly, in a specific implementation a curve-fitting processor compares a curve defined by the echo signal of each location with characteristic curve data being stored in a dedicated memory structure; if the curve of the location fits a curve characteristic of early wash-in, then the location is classified as an early wash-in location, whereas if the same curve of the location fits a curve characteristic of normal tissue, then the location is classified as normal tissue. The pixels of the early wash-in locations so determined are distinctively denoted in the resulting parametric image (in a specific shade, brightness or color).
However, the above-described technique only determines the early wash-in locations; therefore, for each location, nothing more than binary information, indicating whether the curve of the location fits or not the early wash-in characteristic curve, is available.
Alternatively, WO-A1-2006/108868 (the entire disclosure of which is herein incorporated by reference) describes an animated perfusion technique. In this case, a sequence of computed images is generated, by assigning to each pixel thereof an instantaneous value of its model function (at the corresponding instant). Therefore, the display of the computed images provides an animated representation of the evolution over time of any perfusion parameter of interest; this ensures an enhanced visual perception of the perfusion (due to a resulting temporal smoothing, spatial smoothing, and motion removal). Particularly, in a specific implementation a reference function of time is associated with the echo signals in a reference region of the body-part (for example, deemed to be healthy); each pixel of the computed images is then set to the difference between the instantaneous value of its model function and the instantaneous value of the reference function. This facilitates the detection of any locations that exhibit abnormal perfusion kinetics (as compared to the one of the reference region).
Moreover, “Nicolas G. Rognin et al., A New Method for Enhancing Dynamic Vascular Patterns of Focal Liver Lesions in Contrast Ultrasound, 2007 IEEE Ultrasonics Symposium, Piscataway N.J., USA, LNKD-DOI:10.1109 ULTSYM.2007.142, 1 Oct. 2007, pages 546-549, XP031195033ISBN: 978-1-4244-1383-6” (the entire disclosure of which is herein incorporated by reference) proposes generating, for each location, a processed sequence by subtracting a reference signal from the corresponding echo signal; these processed sequences are then used to produce a sequence of computed images.
However, in the above-described techniques the analysis of the body-part requires the display of the whole sequence of computed images (or at least a significant part thereof). Therefore, the analysis process is quite time consuming; moreover, it is not possible to have an overall overview of the results of the analysis process in an immediate way. In any case, the correct assessment of the perfusion kinetics in the different locations of the body-part remains rather challenging; the obtained results are then strongly dependent on personal skills (with an unavoidable rate of errors).